Top AI Engineer Jobs

Leads hands-on security architecture and implementation for AWS cloud, software delivery, applications, and AI platforms. Builds secure-by-default Terraform/OpenTofu controls, identity and access patterns, CI/CD protections, vulnerability management, threat models, AI security evaluations, detection mechanisms, and incident remediation. Partners with engineering, CyberOps, Identity, GRC, and Privacy teams while mentoring engineers and translating security requirements into automated, reusable guardrails.

Lead the enterprise AI Security Program by designing and implementing security standards, controls, guardrails, governance, automation, and monitoring for LLMs, AI platforms, agents, gateways, and AI-enabled applications. Partner with application security, engineering, privacy, compliance, and operations teams to address threats such as prompt injection, jailbreaks, data exposure, excessive agency, and adversarial attacks. Provide technical leadership, threat modeling, security architecture guidance, and maturity reporting.

Designs and develops AI-enabled software for hardware emulation, prototyping, verification, debug, and validation. Builds LLM agents, RAG workflows, and automation services while applying secure and responsible AI practices. Creates testing, evaluation, observability, and regression workflows for AI products, analyzes failures, and improves retrieval and model quality. Provides technical guidance to other engineers and collaborates across teams to deliver reliable, maintainable software.

Develops and manages tooling and machining capabilities for satellite AI-compute hardware. Responsibilities include precision part manufacturing, DFM analysis, machining process controls, equipment procurement and maintenance, supplier coordination, cost optimization, capacity planning, materials research, manufacturing cost estimation, and project scheduling. The role bridges design, manufacturing, machining, quality, and production teams while supporting prototype and high-volume production.

Lead AI engineering for an enterprise AI Center of Excellence, designing and deploying agentic AI solutions from prototype through production. Establish architecture, engineering, evaluation, monitoring, security, and responsible AI standards. Build integrations with enterprise data, ERP, CRM, and collaboration platforms; develop RAG systems, multi-agent workflows, and tool-calling solutions. Partner with business stakeholders, coach distributed teams, mentor AI developers and program managers, and support AI adoption across global manufacturing operations.

Staff Security Engineer responsible for securing AWS cloud, Infrastructure as Code, software delivery, applications, and AI platforms. The role defines security architecture, builds reusable Terraform/OpenTofu controls, strengthens identity and logging, secures GitHub Actions, conducts threat modeling, and develops protections for agentic AI systems. It also leads vulnerability remediation, detection and response improvements, incident root-cause analysis, security evaluations, and compliance evidence generation while mentoring engineers and influencing cross-functional teams.

Develop, deploy, maintain, test, and troubleshoot generative AI frameworks, services, applications, and models for ArcGIS Enterprise, ArcGIS Online, and cloud platforms. Collaborate with global cross-functional teams, ensure software quality and security, and communicate project status. The role requires Python or Java development, containers, Kubernetes, cloud environments, agile practices, and knowledge of generative AI.

Owns security engineering for AI and ML infrastructure, including training and inference pipelines, model registries, MCP servers, and agent runtimes. Designs security controls, conducts threat modeling and adversarial assessments, maintains compliance, and drives vulnerability remediation. Partners with AI Platform, Research, ML Operations, Science, and AI Safety teams to embed security across data, models, pipelines, and agentic systems.

Designs, develops, and secures production-grade generative AI platforms, LLM, RAG, and agentic AI applications. Builds model-serving infrastructure, orchestration frameworks, observability and guardrails, and cloud-native services. Implements AI security, governance, threat modeling, vulnerability remediation, secure deployment, CI/CD, and DevSecOps practices across cloud and hybrid environments.

Designs, builds, and evaluates secure generative AI applications, agentic workflows, and multi-agent systems. Responsibilities include threat modeling, prompt-injection and data-exposure assessment, identity and access controls, tool authorization, execution isolation, monitoring, auditability, and human approval controls. The role develops prototypes, architecture patterns, documentation, and evaluation methods while guiding engineers and leading design reviews. It requires strong Python, AI/ML, cybersecurity, cloud architecture, and governance expertise, with Secret clearance eligibility.

Build secure, scalable full-stack HR applications integrating enterprise AI services, LLMs, RAG pipelines, web technologies, and SQL data stores. Develop APIs, front ends, data tiers, retrieval and ETL pipelines, and AI-enabled workflows. Ensure data quality, privacy, role-based access, auditability, and performance. Collaborate with product, data, and security stakeholders to identify use cases and deliver reliable production solutions.

Lead customer deployments of frontier AI models across semiconductor and AI infrastructure workloads. Scope ambiguous problems, build and debug production software, design reproducible evaluations and benchmarks, validate performance, and support production deployment. Collaborate with customer engineers, researchers, and product teams while contributing to core product improvements. Create reusable tools and playbooks, communicate risks and results, and maintain delivery ownership across workstreams.

Build and maintain full-stack internal network automation and AI-Ops products, including operational dashboards, topology visualizations, real-time data views, conversational AI interfaces, APIs, and data integrations. Integrate AI services, network and inventory platforms, and vendor tools. Use TypeScript, Python, CI/CD, testing, and cloud technologies while translating operational requirements into technical solutions and supporting globally distributed Agile teams.

Lead the architecture of Verizon’s enterprise AI platform across five layers, defining interfaces, standards, security, governance, observability, and scalability patterns. Conduct build-versus-buy evaluations and three-year TCO modeling, develop multi-cloud strategies, and architect LLM, agentic AI, and responsible AI capabilities. Create ADRs and reference architectures, guide reviews, and influence executive and engineering stakeholders. The role requires expertise in distributed systems, Kubernetes, LLM architecture, cloud platforms, API design, enterprise security, and agent orchestration.

Build and scale AI-powered automations and internal tools across Operations, Finance, Marketing, and Customer Success. Translate business problems into pragmatic solutions, integrate LLMs, agents, RAG, APIs, and scripts, and harden prototypes for reliable production use. Coach non-technical employees, develop reusable automation components and templates, monitor workflows, document systems, and shape the company’s automation roadmap.

Designs and operates security controls for AI/ML systems, including threat detection, incident response, cloud and application security, model governance, adversarial testing, and security automation. Builds production tooling across AI platforms such as SageMaker, Azure OpenAI, and Databricks; applies MITRE ATLAS, OWASP AI Top 10, and NIST AI RMF frameworks; and integrates enterprise security platforms to protect models, data, pipelines, and AI applications.

Leads the design, development, and ownership of scalable AI platform services, APIs, microservices, model-serving capabilities, agent runtimes, and lifecycle tooling. Establishes secure cloud-native deployment, observability, reliability, MLOps, and platform operations across cloud, hybrid, on-premises, and restricted environments. Partners with architecture, AI, application engineering, cybersecurity, data, and product teams; makes technical decisions, mentors engineers, and develops reusable enterprise AI capabilities.

Build and optimize high-fidelity document parsing, text extraction, chunking, and metadata-enrichment pipelines for LLMs, vector databases, and RAG systems. Preserve document structure across complex formats, implement multimodal table extraction, benchmark retrieval performance, improve embedding accuracy, and reduce hallucinations through context-preserving processing.

Designs and builds reusable enterprise platform capabilities for production AI agents and multi-agent workflows. Responsibilities include agent orchestration, RAG and knowledge retrieval, tool integration, evaluation, observability, model lifecycle management, APIs, SDKs, CI/CD, security, reliability, and cost optimization. The role converts AI prototypes into scalable cloud-native services and collaborates with engineering, data, product, and architecture teams. It is hybrid in Atlanta, with full-time onsite work required during the first 90 days.

Optimize distributed AI training and inference across CPU/GPU clusters, networking silicon, and software stacks. Benchmark workloads, tune distributed serving and training architectures, analyze communication and system bottlenecks, validate RDMA and collective performance, and create customer-facing proof points. Use profiling and observability tools to visualize performance, compare interconnects, and communicate best practices through technical documentation, publications, and customer engagements.

Supports the development, testing, documentation, and production delivery of AI and machine learning solutions, including generative AI and agent-based applications. Works with data and application engineers to build AI workflows, evaluate outputs, address data quality concerns, follow responsible AI practices, and maintain code quality. The role involves client collaboration with guidance, participation in reviews, and continued development of AI, machine learning, data engineering, and production delivery skills.

Lead technical pre-sales for AI security solutions across SLED accounts. Responsibilities include technical discovery, architecture reviews, proof-of-concepts, product demonstrations, customer and partner training, deployment support, demo environment management, and post-sales handoffs. The role enables sales engineers and channel partners, engages technical decision-makers, and promotes secure deployment of agentic AI, including AI security best practices and integrations.

Provides pre-sales technical leadership for AI security solutions, including technical discovery, architecture reviews, demonstrations, proof-of-concepts, deployment support, partner enablement, and customer training. The role supports sales strategy, develops demo environments, enables regional sales engineers, manages technical handoffs, and builds relationships with enterprise AI and security leaders. Requires AI security expertise, cloud deployment experience, Docker and Kubernetes knowledge, and travel up to 50%.

Build and maintain an AI-powered technical interview pipeline, including question repositories, automated test delivery, answer collection, LLM-based scoring, reporting, and quality controls. Calibrate prompts and models against human-graded samples, monitor accuracy, consistency, bias, and failure modes, and route uncertain cases for human review. Automate internal workflows, document scoring logic, and support Git platform migration and future tooling projects.

Supports pre-sales execution for AI security solutions through technical discovery, architecture reviews, proofs of concept, demonstrations, customer deployments, partner enablement, and technical training. The role develops regional sales engineers, maintains demo environments, supports cloud and self-hosted deployments, leads customer engagements, and builds relationships with technical decision-makers. It also serves as an AI security evangelist focused on agentic AI best practices and secure AI adoption.